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    Coursera
    1. First Steps in Linear Algebra for Machine Learning
    2. Week 4
    3. Life expectancy prediction quiz
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    1.
    Question 1

    The linear regression approximation is

    w^=(XTX)−1XTy\hat{w} = (X^TX)^{-1}X^Tyw^=(XTX)−1XTy

    Based on the difference in prediction and true value, and the train/test data overall, what ways do you think will help improve the Linear regression model?

    Hint: least squares solution tries to aggregate all the given data, even those items that do not seem conform to the general trend. The solution requires finding a matrix inverse, think what properties matrix XXX has to have.

    1 / 1 point
    Check
    Correct

    We won't be able to find the approximation w^\hat{w}w^, if the matrix XTXX^TXXTX is singular.

    Check
    Correct
    2.
    Question 2

    Considering the multi-feature AlgLinearRegression, which 2 features are the most important, i.e. have the most impact on the prediction?

    1 / 1 point
    Check
    Correct

    Use the values in reg.coef_ to determine feature importance. Do not forget, that `coef_` also contains the value for our free variable (intercept).

    Check
    Correct

    Use the values in reg.coef_ to determine feature importance. Do not forget, that `coef_` also contains the value for our free variable (intercept).

    3.
    Question 3

    Consider the multi-feature AlgLinearRegression. What mistakes are there more of, when underpredicting (predicting a value less than the true figure), or overpredicting (predicting a value greater than the true figure)?

    Hint: plot a histogram for the error distribution.

    1 / 1 point
    Check
    Correct

    The y_pred-y_true distribution is resembling normal distribution.

    4.
    Question 4

    Looking at the SVM classifier coefficients, which feature is the most important in determining an object class?

    1 / 1 point
    Check
    Correct

    Use the "Feature importnaces for SVM" plot.